The TUBE algorithm: Discovering trends in time series for the early detection of fuel leaks from underground storage tanks

Gorawski, Marcin; Gorawska, Anna; Pasterak, Krzysztof

doi:10.1016/j.eswa.2017.08.016

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…It is possible that this result stems from the highly deformed tank from which the data is obtained. Whereas the previous studies worked with a volume difference of a few liters (Gorawski et al, 2017;Z. Li et al, 2011) that of current data is within the range of +100 L to -300 L. Thus, we believe that in the case of a tank with a lower variance range, the method would produce acceptable results well below the threshold of 0.4 L/h.…”

Section: Variance Reduction/adjustmentmentioning

confidence: 61%

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Yakıt Tankları İçin Bir Stok Yönetimi ve Sızıntı Tespit Sistemi

Dalman¹,

Özbek²

2020

Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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One of the critical problems in wet-stock management is inaccurate (poor) tank calibration that masks the leakages from the underground storage tanks (USTs). Moreover, obtaining the correct tank parameters or recalibration is an expensive procedure if not impossible. This study aims to prevent the masking effect of several tank parameters on the tank calibration chart and to improve the leak detection and wet-stock management for fuel storage tanks. This goal is achieved through obtaining the mathematical models for simple cylindrical tank that convert the measured liquid height to accurate liquid volume by taking into account the tank deformations. The accounted deformations in tank parameters are errors in radius, length, probe offset and axial and radial tank tilts, as well as volume change with temperature. The simulations using the actual data gathered from a commercial fuel service station showed that the approach developed in this study is valid. The deformation included tank models successfully predicted the real fuel volumes. The results showed that the variance reduced from-300 L-+100 L range to-20 L-+20 L range for, which brings 67.2% improvement over the cumulative variance. This study also shows that obtaining the precise volume measurements by introducing models that account for the deformations in the UST is possible.

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Section: Variance Reduction/adjustmentmentioning

confidence: 61%

“…Li, 2014;Nosach, 2000;Sigut, Alayón, & Hernández, 2014). Second, from the wet-stock management and leak detection point of view, the reduced variance (67.2%) appears to be a good improvement compared to similar works (Caihong Gorawski et al, 2017;Z. Li, Shui, Luo, Chen, & Li, 2011;Sigut et al, 2014).…”

Section: Variance Reduction/adjustmentmentioning

confidence: 91%

Yakıt Tankları İçin Bir Stok Yönetimi ve Sızıntı Tespit Sistemi

Dalman¹,

Özbek²

2020

Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…does not require specialized equipment are also possible (Gorawski et al, 2017). The leak threshold for tank and pipe varies by techniques, whose number exceeds 390 (NWGLDE, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Wet-Stock Management and Leak Detection System for Fuel Tanks

Dalman

Özbek

2020

Journal of Advanced Research in Natural and Applied Sciences

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One of the critical problems in wet-stock management is inaccurate (poor) tank calibration that masks the leakages from the underground storage tanks (USTs). Moreover, obtaining the correct tank parameters or re-calibration is an expensive procedure if not impossible. This study aims to prevent the masking effect of several tank parameters on the tank calibration chart and to improve the leak detection and wet-stock management for fuel storage tanks. This goal is achieved through obtaining the mathematical models for simple cylindrical tank that convert the measured liquid height to accurate liquid volume by taking into account the tank deformations. The accounted defor-mations in tank parameters are errors in radius, length, probe offset and axial and radial tank tilts, as well as volume change with temperature. The simulations using the actual data gathered from a commercial fuel service station showed that the approach developed in this study is valid. The deformation included tank models successfully pre-dicted the real fuel volumes. The results showed that the variance reduced from -300 L – +100 L range to -20 L – +20 L range for, which brings 67.2% improvement over the cumulative variance. This study also shows that obtaining the precise volume measurements by introducing models that account for the deformations in the UST is possible.

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“…A good representation of such infrastructure is very helpful in locating, e.g., leakage in these systems, especially leaks in gas and fuel lines are very dangerous and need to be quickly localized. As the inconsistencies can exist from nay different sources such as malfunctioning sensor or miscalculation of equipment [3], it is important to supply effective algorithms that identify real leaks without false positive alerts [4]. As all the above-mentioned infrastructures are defined as an LI, their characteristics can be easy translated into graphs.…”

Section: Introductionmentioning

confidence: 99%

Performance Tests of Smart City IoT Data Repositories for Universal Linear Infrastructure Data and Graph Databases

Gorawski

Grochla

2019

SN COMPUT. SCI.

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The infrastructure managed by Smart City systems includes mainly linear structures, such as pipelines, electric power lines, railway lines, etc.. In this paper, we propose the transcription of linear infrastructure to graph representation and storing needed meta-data in a graph database, merged with the traditional relational database used for device management. Such an approach enables fast acquisition of data about infrastructure's properties and allows merging information about the structure of the managed infrastructure with information devices' properties and statistics. We develop a graph generator that generates virtual network basing on representative examples of linear infrastructure, incorporation of a data from existing metering systems with the generated structure, which automates the deployment and allows to evaluate the performance of Smart City monitoring and management system. We present results of performance tests that show and the creation time of graph database structures in Neo4j and the difference between the performance of the Microsoft SQL Server database and the Neo4j database in a few Smart City use cases. The results show that the use of the graph database to execute queries related to linear infrastructure allows decreasing the response time up to 9 times.

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The TUBE algorithm: Discovering trends in time series for the early detection of fuel leaks from underground storage tanks

Cited by 15 publications

References 30 publications

Yakıt Tankları İçin Bir Stok Yönetimi ve Sızıntı Tespit Sistemi

Yakıt Tankları İçin Bir Stok Yönetimi ve Sızıntı Tespit Sistemi

A Wet-Stock Management and Leak Detection System for Fuel Tanks

Performance Tests of Smart City IoT Data Repositories for Universal Linear Infrastructure Data and Graph Databases

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